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Fundamentals of the PhD education at SCIENCE - module 2 - K14
Provider: Faculty of Science

Activity no.: 5044-27-00-01
	Enrollment deadline: 05/04/2027

Date and time	05.04.2027, at: 00:00 - 09.04.2027, at: 00:00
Regular seats	50
ECTS credits	2.50

Contact person	Fundamentals secretary group E-mail address: phdfundamentals@science.ku.dk

Enrolment Handling/Course Organiser	Fundamentals secretary group E-mail address: phdfundamentals@science.ku.dk

Enrolment guidelines 1: The module is mandatory for all PhD students at the Faculty of SCIENCE enrolled after 1 January 2024. 2: PhD students enrolled before 1 January 2024 and External PhD students do not participate in Fundamentals. 3: Fundamentals modules 1, must be passed before you can take module 2. Module 2 lies 6 months into the PhD programme. If you are enrolled between 1 January 2024 and February 2025 and need to sign up for one or more Fundamentals modules, please contact the PhD course administration - phdfundamentals@science.ku.dk If you are enrolled after 1 March 2025, you will automatically be signed up for all 4 Fundamentals modules. Content The purpose of the course is to introduce the students into Data Science, Data Management, and Career Management: • Many students will apply/develop Data Science methods (data analysis, statistics, and machine learning) directly in their own research; and all students should be aware of this potential. • Some students will directly apply Data Management principles, and all students should be aware of the general policies. • All students should actively manage their careers. Data Management The aim of the Data Management segment is to equip PhD students with knowledge and skills to: • Manage data and primary materials responsibly during their PhD projects. • Create open and reproducible research outputs. Career Management The aim of the Career Management segment is that PhD students start to explore their values, motivation, and the great variety of career options that are open to them after their PhD. The segment will cover: • Megatrends in the labour market and typical career paths for PhDs from the natural sciences. • Motivation, values, and career priorities. • Change of scientific environment. • Networking for career development. The Data Science segment will aim ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics or Machine Learning methods when appropriate in their research. Statistics The Statistics variant will cover: • Statistical thinking and methodology, including statistical power and principles for experimental design. • Statistical modelling using fixed and random effects. • Tabular and graphical presentation of experimental results and statistical analyses. • The R programming environment for Statistics and basic comparisons to other statistical software. Machine Learning The Machine Learning variant will cover: • Machine learning foundations and methods. • Data Science caveats and best practice. • Introduction into AI with focus on intuitive understanding the big models in terms of potential, applicability, limitations, and sustainability. • Application of AI and machine learning in science. Learning objectives The learning outcomes for the segments are given below. Each outcome is marked K for Knowledge, S for Skills, or C for Competences. Data Management segment: • Identify relevant legislation, requirements, and policies on data management applicable to research projects at UCPH (K). • Recognize recommendations and requirements regarding open and reproducible research designs, data collection, and data publication (K). • Classify data and conduct a risk assessment to ensure the secure storage of data (S). • Assess how data can be preserved and shared to guarantee FAIR use of data (S). • Assess when to use electronic lab notes (S). • Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C). • Be able to adhere to best practices of open and reproducible research (C). Career Management segment: • Differentiate typical career paths for SCIENCE PhDs (K). • Describe selected understandings of motivation (K). • Explore and explain their career priorities (S). • Build a professional profile on LinkedIn (S). • Assess how different uses of their change of scientific environment may impact their career (S). • Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C). • Develop a personal networking strategy and build a professional network that supports their career interests (C). Data Science segment: • Know Data Science as a research methodology (K). • Apply basic Statistics or Machine Learning computational frameworks and methods when appropriate in their research (S). • Build a network for potential inter-disciplinary data science collaborations (C). Teaching and Learning Methods The module will include a mixture of lectures, exercises, plenary and group discussions, and peer feedback during the on-campus segments. Around the on-campus week, the students will perform eLearning as well as reflective and practical assignments. Type of assessment All segments require presence and active participation. The students must hand in a report where they choose between a Data Management Plan, or a report related to their Statistics or Machine Learning specialization Language English Course days The module consists of 10 morning/afternoon sessions during the on-campus course week (2 on Data Management, 1 on Career Management, and 7 on either Machine Learning or Statistics). Prior to course start, the students will choose to follow either the Statistics or Machine Learning variant of the module. Remarks The PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes, includes, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision, tool-box and specialized PhD courses. Moreover, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. Cancellation policy • Cancellations made up to two weeks before the course starts are free of charge. • Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000 • Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000 • No-show will result in a fee of DKK 5.000 • Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000

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